There are two competing philosophies of statistical analysis: the Bayesian and the frequentist. The frequentists are much the larger group, and almost all the statistical analyses which appear in the BMJ are frequentist. The Bayesians are much fewer and until recently could only snipe at the frequentists from the high ground of university departments of mathematical statistics. Now the increasing power of computers is bringing Bayesian methods to the fore.Bayesian methods are based on the idea that unknown quantities, such as population means and proportions, have probability distributions. The probability distribution for a population proportion expresses our prior knowledge or belief about it, before we add the knowledge which comes from our data. For example, suppose we want to estimate the prevalence of diabetes in a health district. We could use the knowledge that the percentage of diabetics in the United Kingdom as a whole is about 2%, so we expect the prevalence in our health district to be fairly similar. It is unlikely to be 10%, for example. We might have information based on other datasets that such rates vary between 1% and 3%, or we might guess that the prevalence is somewhere between these values. We can construct a prior distribution which summarises our beliefs about the prevalence in the absence of specific data. We can do this with a distribution having mean 2 and standard deviation 0.5, so that two standard deviations on either side of the mean are 1% and 3%. (The precise mathematical form of the prior distribution depends on the particular problem.)Suppose we now collect some data by a sample survey of the district population. We can use the data to modify the prior probability distribution to tell us what we now think the distribution of the population percentage is; this is the posterior distribution. For example, if we did a survey of 1000 subjects and found 15 (1.5%) to be diabetic, the posterior distribution would have mean 1.7% and standard deviation 0.3%. We can calculate a set of values, a 95% credible interval (1.2% to 2.4% for the example), such that there is a probability of 0.95 that the percentage of diabetics is within this set. The frequentist analysis, which ignores the prior information, would give an estimate 1.5% with standard error 0.4% and 95% confidence interval 0.8% to 2.5%. This is similar to the results of the Bayesian method, as is usually the case, but the Bayesian method gives an estimate nearer the prior mean and a narrower interval.Frequentist methods regard the population value as a fixed, unvarying (but unknown) quantity, without a probability distribution. Frequentists then calculate confidence intervals for this quantity, or significance tests of hypotheses concerning it. Bayesians reasonably object that this does not allow us to use our wider knowledge of the problem. Also, it does not provide what researchers seem to want, which is to be able to say that there is a probability of 95% that the population value lies within the 95% confidence interval, or that...
Medical Education 2010: 44 : 165–176 Context The effectiveness of multi‐source feedback (MSF) tools, which are increasingly important in medical careers, will be influenced by their users’ attitudes. This study compared perceptions of two tools for giving MSF to UK junior doctors, of which one provides mainly textual feedback and one provides mainly numerical feedback. We then compared the perceptions of three groups, including: trainees; raters giving feedback, and supervisors delivering feedback. Methods Postal questionnaires about the usability, usefulness and validity of a feedback system were distributed to trainees, raters and supervisors across the north of England. Results Questionnaire responses were analysed to compare opinions of the two tools and among the different user groups. Overall there were few differences. Attitudes towards MSF in principle were positive and the tools were felt to be usable, but there was little agreement that they could effectively identify doctors in difficulty or provide developmental feedback. The text‐oriented tool was rated as more useful for giving feedback on communication and attitude, and as more useful for identifying a doctor in difficulty. Raters were more positive than other users about the usefulness of numerical feedback, but, overall, text was felt to be more useful. Some trainees expressed concern that feedback was based on insufficient knowledge of their work. This was not supported by raters’ responses, although many did use indirect information. Trainees selected raters mainly for the perceived value of their feedback, but also based on personal relationships and the simple pragmatics of getting a tool completed. Discussion Despite positive attitudes to MSF, the perceived effectiveness of the tools was low. There are small but significant preferences for textual feedback, although raters may prefer numerical scales. Concerns about validity imply that greater awareness of contextual and psychological influences on feedback generation is necessary to allow the formative benefits of MSF to be optimised and to negate the risk of misuse in high‐stakes contexts.
Doctors should consider the transactional or relational preference of a patient in approaching a consultation. Patient feedback can deliver benefits to doctors and patients, but risks must be acknowledged and mitigated against.
Forty-eight consecutive patients not taking dopamine antagonists and without Parkinson's disease referred to a psychogeriatric service with a diagnosis of psychiatric disorder were assessed for affective flattening using an objective rating scale. Nearly half (44%) exhibited significant affective flattening, which proved open to reliable assessment. Affective flattening is a useful sign of pathology.
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